Logging

Logging is the practice of emitting timestamped records of events from running software so operators can reconstruct what happened. Modern systems use structured logging , where each record is a JSON object with named fields (level, timestamp, service, request_id, message, plus arbitrary attributes), making logs queryable rather than just human-readable.

Log levels

• TRACE / DEBUG: fine-grained, developer-focused, usually off in production
• INFO: normal operational events worth noting
• WARN: something unexpected but recoverable
• ERROR: a request or job failed
• FATAL: the process cannot continue

Conventions that matter

• Structured fields, not string interpolation. log.info({user_id, action}) beats log.info("user 123 did x") because it is queryable.
• Correlation IDs. A request_id or trace_id threaded through every log line links a single request across services.
• PII discipline. Never log credentials, tokens, or sensitive customer fields.
• Sampling at volume. High-traffic services drop a fraction of INFO logs to control cost; ERRORs are always kept.

Common tools

• Libraries: pino (Node), zap (Go), structlog (Python), Logback (Java), tracing (Rust)
• Aggregators: Loki, Elasticsearch (ELK), OpenSearch, Datadog, Splunk, Sumo Logic, Better Stack

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Related Terms Observability, Metrics, Distributed Tracing, OpenTelemetry

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Further Reading Structured Logging Explained: Levels, Examples, and Best Practices